Content Trend Researcher

What it does

When your agent uses it

• Tasks that involve Newsletters
• Tasks that involve Podcasting

Example prompts

• “/content-trend-researcher”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Identify trending topics - Find what's gaining traction across platforms
2. 2Understand user intent - Analyze search patterns and engagement signals
3. 3Discover content gaps - Find underserved topics with high demand
4. 4Generate outlines - Create data-driven article structures optimized for engagement
5. 5Platform-specific insights - Understand where different content types perform best

What it can do on your machine

Read from SKILL.md and the folder at commit ba18b31. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Python), which the agent can run.

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Content Trend Researcher loads about 2.1k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 637 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~67

When it runs · the whole SKILL.md, loaded when a task matches

~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from alirezarezvani/claude-code-skill-factory at commit ba18b31, republished under its MIT licence (© alirezarezvani). 637 words, ~2,060 tokens.

name

content-trend-researcher

description

Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis

@content-trend-researcher

Topic: "AI automation for small businesses"
Platforms: Google Trends, Reddit, LinkedIn, YouTube
Intent: Informational

@content-trend-researcher

Topic: "Remote work productivity tools"
Platforms: ALL
Include: User intent breakdown, content gap analysis, 3 article outlines
Target audience: SaaS founders and product managers

@content-trend-researcher

Topic: "Email marketing strategies 2025"
Platforms: Medium, Substack, Top blogs
Analyze: Top 10 performing articles
Output: Outline that fills content gaps

@content-trend-researcher

Topic: "Sustainable fashion"
Platforms: Instagram, TikTok, YouTube, Pinterest, Blogs
Output: Platform-specific content ideas + 1 long-form blog outline

{
  "topic": "Your main topic or keyword",
  "platforms": ["Google Trends", "Reddit", "YouTube", "etc."],
  "intent_focus": "informational|commercial|transactional|all",
  "target_audience": "Description of your audience",
  "content_type": "blog|article|newsletter|video script|social post",
  "analysis_depth": "quick|standard|deep",
  "number_of_outlines": 1-5
}

{
  "topic_overview": {
    "search_volume": "Monthly search volume estimate",
    "trend_direction": "rising|stable|declining",
    "competition_level": "low|medium|high",
    "opportunity_score": 1-100
  },
  "platform_insights": [
    {
      "platform": "Platform name",
      "trending_content": [],
      "engagement_metrics": {},
      "best_practices": [],
      "content_format": "Recommended format"
    }
  ],
  "user_intent_analysis": {
    "primary_intent": "Intent type",
    "intent_breakdown": {
      "informational": "percentage",
      "commercial": "percentage",
      "transactional": "percentage"
    },
    "top_questions": [],
    "search_patterns": []
  },
  "content_gaps": [
    {
      "gap": "Description of underserved topic",
      "opportunity": "Why this is valuable",
      "difficulty": "low|medium|high"
    }
  ],
  "article_outlines": [
    {
      "title": "SEO-optimized title",
      "subtitle": "Engaging subtitle",
      "target_word_count": 1500-2000,
      "structure": [
        {
          "heading": "H2 heading",
          "subheadings": ["H3", "H3"],
          "key_points": [],
          "research_notes": "Data supporting this section"
        }
      ],
      "seo_keywords": [],
      "internal_links": [],
      "multimedia_suggestions": [],
      "cta": "Suggested call-to-action"
    }
  ],
  "recommendations": {
    "publishing_schedule": "Best days/times",
    "content_format": "Recommended format",
    "promotion_strategy": "Where to share",
    "follow_up_topics": []
  }
}

© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files in generated-skills/content-trend-researcher of alirezarezvani/claude-code-skill-factory.

• SKILL.md
• HOW_TO_USE.md
• expected_output.json
• intent_analyzer.py
• outline_generator.py
• platform_insights.py
• sample_input.json
• trend_analyzer.py

Open the folder on GitHubat commit ba18b31

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in alirezarezvani/claude-code-skill-factory, which our catalogue first saw on October 7, 2026.

• nicepkg/ai-workflowworkflows/video-creator-workflow/.claude/skills/content-trend-researcherworkflows/content-creator-workflow/.claude/skills/content-trend-researcher